




"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor



class   a__					(						unittest.TestCase ):


			def __init__(   self   :     List[Any]   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     Tuple=3   ,	UpperCamelCase_   :     Optional[int]=32   ,	UpperCamelCase_   :     Dict=3   ,	UpperCamelCase_   :     List[str]=10   ,	UpperCamelCase_   :     str=[10, 20, 30, 40]   ,	UpperCamelCase_   :     Tuple=[1, 1, 2, 1]   ,	UpperCamelCase_   :     str=True   ,	UpperCamelCase_   :     Optional[int]=True   ,	UpperCamelCase_   :     Dict="relu"   ,	UpperCamelCase_   :     str=3   ,	UpperCamelCase_   :     int=None   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       parent
						__UpperCAmelCase						:			List[str]												=       batch_size
						__UpperCAmelCase						:			List[str]												=       image_size
						__UpperCAmelCase						:			Tuple												=       num_channels
						__UpperCAmelCase						:			Union[str, Any]												=       embeddings_size
						__UpperCAmelCase						:			Dict												=       hidden_sizes
						__UpperCAmelCase						:			Dict												=       depths
						__UpperCAmelCase						:			Tuple												=       is_training
						__UpperCAmelCase						:			List[Any]												=       use_labels
						__UpperCAmelCase						:			Optional[int]												=       hidden_act
						__UpperCAmelCase						:			str												=       num_labels
						__UpperCAmelCase						:			Optional[int]												=       scope
						__UpperCAmelCase						:			Dict												=       len(UpperCamelCase_)


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

						__UpperCAmelCase						:			Dict												=       self.get_config()

						return config, pixel_values


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						return RegNetConfig(
						    num_channels=self.num_channels   ,	embeddings_size=self.embeddings_size   ,	hidden_sizes=self.hidden_sizes   ,	depths=self.depths   ,	hidden_act=self.hidden_act   ,	num_labels=self.num_labels   ,	image_size=self.image_size   ,	)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     Union[str, Any]   ,	UpperCamelCase_   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       FlaxRegNetModel(config=UpperCamelCase_)
						__UpperCAmelCase						:			Dict												=       model(UpperCamelCase_)

						# Output shape (b, c, h, w)
						self.parent.assertEqual(
						    result.last_hidden_state.shape   ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,	)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       self.num_labels
						__UpperCAmelCase						:			Tuple												=       FlaxRegNetForImageClassification(config=UpperCamelCase_)
						__UpperCAmelCase						:			str												=       model(UpperCamelCase_)
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels))


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self.prepare_config_and_inputs()
						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       config_and_inputs
						__UpperCAmelCase						:			Optional[Any]												=       {"pixel_values": pixel_values}
						return config, inputs_dict






@require_flax
class   a__					(						__magic_name__				,     unittest.TestCase ):
			lowercase_		=						(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			lowercase_		=						False
			lowercase_		=						False
			lowercase_		=						False


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       FlaxRegNetModelTester(self)
						__UpperCAmelCase						:			str												=       ConfigTester(self   ,	config_class=UpperCamelCase_   ,	has_text_modality=UpperCamelCase_)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						return


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase_)


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_)


			@unittest.skip(reason="RegNet does not use inputs_embeds")
			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						pass


			@unittest.skip(reason="RegNet does not support input and output embeddings")
			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     str):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									__UpperCAmelCase						:			int												=       model_class(UpperCamelCase_)
									__UpperCAmelCase						:			Optional[int]												=       inspect.signature(model.__call__)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__UpperCAmelCase						:			Any												=       [*signature.parameters.keys()]

									__UpperCAmelCase						:			Dict												=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]   ,	UpperCamelCase_)


			def    a_  (   self   :     int):


						"""simple docstring"""




						def check_hidden_states_output(UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     Union[str, Any]):
									__UpperCAmelCase						:			Union[str, Any]												=       model_class(UpperCamelCase_)

									__UpperCAmelCase						:			Optional[Any]												=       model(**self._prepare_for_class(UpperCamelCase_   ,	UpperCamelCase_))

									__UpperCAmelCase						:			List[str]												=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									__UpperCAmelCase						:			str												=       self.model_tester.num_stages
									self.assertEqual(len(UpperCamelCase_)   ,	expected_num_stages + 1)

						__UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									__UpperCAmelCase						:			List[str]												=       True
									check_hidden_states_output(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__UpperCAmelCase						:			Optional[int]												=       True

									check_hidden_states_output(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
												__UpperCAmelCase						:			List[Any]												=       self._prepare_for_class(UpperCamelCase_   ,	UpperCamelCase_)
												__UpperCAmelCase						:			Optional[int]												=       model_class(UpperCamelCase_)

												@jax.jit
												def model_jitted(UpperCamelCase_   :     int   ,	**UpperCamelCase_   :     Optional[int]):
															return model(pixel_values=UpperCamelCase_   ,	**UpperCamelCase_)

												with self.subTest("JIT Enabled"):
															__UpperCAmelCase						:			Optional[Any]												=       model_jitted(**UpperCamelCase_).to_tuple()

												with self.subTest("JIT Disabled"):
															with jax.disable_jit():
																		__UpperCAmelCase						:			Dict												=       model_jitted(**UpperCamelCase_).to_tuple()

												self.assertEqual(len(UpperCamelCase_)   ,	len(UpperCamelCase_))
												for jitted_output, output in zip(UpperCamelCase_   ,	UpperCamelCase_):
															self.assertEqual(jitted_output.shape   ,	output.shape)






def        _UpperCamelCase     (      )      ->      Any:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[Any]												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
			return image





@require_flax
class   a__					(						unittest.TestCase ):


			@cached_property
			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None


			@slow
			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

						__UpperCAmelCase						:			Dict												=       self.default_image_processor
						__UpperCAmelCase						:			str												=       prepare_img()
						__UpperCAmelCase						:			int												=       image_processor(images=UpperCamelCase_   ,	return_tensors="np")

						__UpperCAmelCase						:			Dict												=       model(**UpperCamelCase_)

						# verify the logits
						__UpperCAmelCase						:			Dict												=       (1, 1000)
						self.assertEqual(outputs.logits.shape   ,	UpperCamelCase_)

						__UpperCAmelCase						:			Any												=       jnp.array([-0.4180, -1.5051, -3.4836])

						self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,	UpperCamelCase_   ,	atol=1e-4))










'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


